• Journal of Information Processing Systems
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• v.15 no.1
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• pp.1-6
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• 2019

Recently, big data and artificial intelligence (AI) based on communication systems have become one of the hottest issues in the technology sector, and methods of analyzing big data using AI approaches are now considered essential. This paper presents diverse paradigms to subjects which deal with diverse research areas, such as image segmentation, fingerprint matching, human tracking techniques, malware distribution networks, methods of intrusion detection, digital image watermarking, wireless sensor networks, probabilistic neural networks, query processing of encrypted data, the semantic web, decision-making, software engineering, and so on.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.30-36
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• 2006

In this paper, a new type of written image segmentation based on relative comparison of region areas is proposed. The original image is composed of two distinctive regions; information and background. Compared with this binary original image, the observed one is the gray scale which is represented with complex regions with speckles and noise due to degradation or contamination. For applying threshold or statistical approach, there occurs the region-deformation problem in the process of binarization. At first step, the efficient iterated conditional mode (ICM) which takes the lozenge type block is used for regions formation into the binary image. Secondly the information region is estimated through selecting action and restored its primary state. Not only decision of the attachment to a region but also the calculation of the magnitude of its area are carried on at each current pixel iteratively. All region areas are sorted into a set and selected through the decision parameter which is obtained statistically. Our experiments show that these approaches are effective on ink-rubbed copy image (拓本 'Takbon') and efficient at shape restoration. Experiments on gray scale image show promising shape extraction results, comparing with the threshold-segmentation and conventional ICM method.

• Land and Housing Review
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• v.14 no.4
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• pp.95-102
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• 2023

Numerous investigations have explored the integration of machine leaning algorithms with high-resolution drone image for object detection in urban settings. However, a prevalent limitation in vehicle extraction studies involves the reliance on bounding boxes rather than instance segmentation. This limitation hinders the precise determination of vehicle direction and exact boundaries. Instance segmentation, while providing detailed object boundaries, necessitates labour intensive labelling for individual objects, prompting the need for research on automating unsupervised instance segmentation in vehicle extraction. In this study, a novel approach was proposed for vehicle extraction utilizing unsupervised SVM classification applied to vehicle bounding boxes in drone images. The method aims to address the challenges associated with bounding box-based approaches and provide a more accurate representation of vehicle boundaries. The study showed promising results, demonstrating an 89% accuracy in vehicle extraction. Notably, the proposed technique proved effective even when dealing with significant variations in spectral characteristics within the vehicles. This research contributes to advancing the field by offering a viable solution for automatic and unsupervised instance segmentation in the context of vehicle extraction from image.

• Journal of the Korea Computer Graphics Society
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• v.22 no.3
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• pp.21-29
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• 2016

Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.

• The Journal of the Acoustical Society of Korea
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• v.25 no.5
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• pp.213-221
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• 2006

Phone segmentation of speech waveform is especially important for concatenative text to speech synthesis which uses segmented corpora for the construction of synthetic units. because the quality of synthesized speech depends critically on the accuracy of the segmentation. In the beginning. the phone segmentation was manually performed. but it brings the huge effort and the large time delay. HMM-based approaches adopted from automatic speech recognition are most widely used for automatic segmentation in speech synthesis, providing a consistent and accurate phone labeling scheme. Even the HMM-based approach has been successful, it may locate a phone boundary at a different position than expected. In this paper. we categorized adjacent phoneme pairs and analyzed the mismatches between hand-labeled transcriptions and HMM-based labels. Then we described the dominant error patterns that must be improved for the speech synthesis. For the experiment. hand labeled standard Korean speech DB from ETRI was used as a reference DB. Time difference larger than 20ms between hand-labeled phoneme boundary and auto-aligned boundary is treated as an automatic segmentation error. Our experimental results from female speaker revealed that plosive-vowel, affricate-vowel and vowel-liquid pairs showed high accuracies, 99%, 99.5% and 99% respectively. But stop-nasal, stop-liquid and nasal-liquid pairs showed very low accuracies, 45%, 50% and 55%. And these from male speaker revealed similar tendency.

• Journal of the Korean association of regional geographers
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• v.11 no.5
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• pp.426-439
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• 2005

The purpose of this study was to analyze the perceptions of local residents towards the tourism impacts. The region of this study, Anmyeon-do has been developed as an international tourist destination since 1993. This study was trying to estimate the perception differences between groups of resident in teams of socio-demographics, geographic characteristics and psycho-behavioral characteristics. Self-administered questionnaire survey was administered for the residents. Frequency analysis, factor analysis, t-test, and ANOVA was tested for the perception differences. Using segmentation approaches, it was revealed that geographic characteristics and psycho-behavioral characteristics were relatively useful to test the perceptions differences of residents towards tourism impacts. Implications are drawn for regional tourism policy and management.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.822-828
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• 2002

Optical flow estimation based on multi constraint approaches is frequently used for recognition of moving objects. However, the use have been confined because of OF estimation time as well as error problem. This paper shows a new method form effectively extracting movement information using the multi-constraint base approaches with sobel edge detection. The moving objects anr extraced in the input image sequence using edge detection and segmentation. Edge detection and difference of the two input image sequence gives us the moving objects in the images. The process of thresholding removes the moving objects detected due to noise. After thresholding the real moving objects, we applied the Combinatorial Hough Transform (CHT) and voting accumulation to find the optimal constraint lines for optical flow estimation. The moving objects found in the two consecutive images by using edge detection and segmentation greatly reduces the time for comutation of CHT. The voting based CHT avoids the errors associated with least squares methods. Calculation of a large number of points along the constraint line is also avoided by using the transformed slope-intercept parameter domain. The simulation results show that the proposed method is very effective for extracting optical flow vectors and hence recognizing moving objects in the images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2665-2691
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• 2024

The philosophy of Zero Trust in cybersecurity lies in the notion that nothing assumes to be trustworthy by default. This drives defense organizations to modernize their cybersecurity architecture through integrating with the zero-trust principles. The enhanced architecture is expected to shift protection strategy from static and perimeter-centric protection to dynamic and proactive measures depending on the logical contexts of users, assets, and infrastructure. Given the domain context of defense environment, we aim three challenge problems to tackle and identify four technical approaches by the security capabilities defined in the Zero Trust Architecture. First approach, dynamic access control manages visibility and accessibility to resources or services with Multi Factor Authentication and Software Defined Perimeter. Logical network separation approach divides networks on a functional basis by using Software Defined Network and Micro-segmentation. Data-driven analysis approach enables machine-aided judgement by utilizing Artificial Intelligence, User and Entity Behavior Analytics. Lastly, Security Awareness approach observes fluid security context of all resources through Continuous Monitoring and Visualization. Based on these approaches, a comprehensive study of modern technologies is presented to materialize the concept that each approach intends to achieve. We expect this study to provide a guidance for defense organizations to take a step on the implementation of their own zero-trust architecture.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1B
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• pp.101-110
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• 2011

With the deployment of broadband networking technology, many clients are enabled to receive various Video on Demand (VoD) services. To support many clients, the network should be designed by considering the following factors: viewer's waiting time, buffer requirement at each client, number of channel required for video delivery, and video segmentation complexity. Among the currently available VoD service approaches, the Polyharmonic and Staircase broadcasting approaches show best performance with respect to each viewer's waiting time and buffer requirement, respectively. However, these approaches have the problem of dividing a video into too many segments, which causes very many channels to be managed and used at a time. To overcome this problem, we propose Polyharmonic-Staircase-Staggered (PSS) broadcasting approach that uses the Polyharmonic and Staircase approaches for the head part transmission and the Staggered approach for the tail part transmission. It is simple and bandwidth efficient. The numerical results demonstrate that our approach shows viewer's waiting time is comparable to that in the Harmonic approach with a slight increase in the bandwidth requirement, and saves the buffer requirement by about 60\% compared to the Harmonic broadcasting approach by simply adjusting the video partitioning coefficient factor. More importantly, our approach shows the best performance in terms of the number of segments and the number of channels managed and used simultaneously, which is a critical factor in real operation of VoD services. Lastly, we present how to configure the system adaptively according to the video partitioning coefficient.

• International Journal of Contents
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• v.7 no.4
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• pp.24-29
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• 2011

In this paper, we propose a method to segment the bottle label in images taken by mobile phones using multi-gradient approaches. In order to segment the label region of interest-object, the saliency map method and Hough Transformation method are first applied to the original images to obtain the candidate region. The saliency map is used to detect the most salient area based on three kinds of features (color, orientation and illumination features). The Hough Transformation is a technique to isolated features of a particular shape within an image. Therefore, we utilize it to find the left and right border of the bottle. Next, we segment the label based on the gradient information obtained from the structure tensor method and edge method. The experimental results have shown that the proposed method is able to accurately segment the labels as the first step of product label recognition system.